B340723.pdf

American Journal of Multidisciplinary Research & Development (AJMRD)
Volume 3, Issue 04 (April- 2021), PP 07-23
ISSN: 2360-821X
www.ajmrd.com
Multidisciplinary Journal www.ajmrd.com Page | 7
Research Paper Open Access
The impact of global production sharing on employment and
wages in the manufacturing industry in Sri Lanka
Sajith de Mel, Ph.D
Abstract: This paper examines the impact of global production sharing on employment and wages in the
manufacturing industry in Sri Lanka. The study models the effects of global production sharing on employment
and wages in the manufacturing industry in a labour demand and a wages framework on an integrated panel
dataset of trade, labour and manufacturing industries. The impact of global production sharing employment and
wages in the manufacturing industry is examined following the predictions of global production sharing models.
On the labour demand model, global production sharing is negative and statistically significant on manufacturing
employment against theoretical expectations. On the wages model, global production sharing is negative and
statistically insignificant on manufacturing wages as opposed to theoretical predictions.
Key words: Trade, Global production sharing, Manufacturing, Employment, Wages
JEL code: F16
I. Introduction
Global production sharing involves the splitting up of the production process and the distribution of the
same to partner countries. This has enabled developing economies to cater to international markets in their
segment of comparative advantage. In the case of Sri Lanka, the share of exports in global production sharing in
total manufacturing exports has continued to decline as a direct result of the trade policies that have been
followed since 2000. The share of exports in global production sharing in total manufacturing exports reduced
from 58.6 percent in 1997 to 47.1 per cent by 2011. Considering the share of exports in parts and components
for 2011, 22.6 percent are concentrated into manufacture of rubber products, followed by exports in electricity
distribution and control apparatus (18.4 percent), electric motors (10.9 percent), manufacture of other electrical
equipment (10.3 percent). Sri Lanka also imports a variety of parts and components essential for manufacturing.
The concentration of imports in parts and components is seen to be steadily increasing in the case of
manufacture of wearing apparel (International Standard Industrial Classification (ISIC)-1810, manufacture of
rubber products (ISIC-2519), manufacture of plastic products (ISIC-2520), manufacture of cutlery, hand tools
and general hardware (ISIC-2893), manufacture of machinery for mining, quarry and construction (ISIC-2924),
manufacture of parts and accessories for motor vehicles and engines (ISIC-3430). As an example, the share of
imports in parts and components in accessories for motor vehicles has increased from a share of 6.24 percent to
8.21 percent from 1996 to 2011. On the other hand, the import of parts and components in the manufacture of
other textiles (ISIC-1729), manufacture of engines and turbines (ISIC-2911), manufacture of other special
purpose machinery (ISIC-2929), manufacture of office, accounting and computing machinery (ISIC-3000),
manufacture of electric generators, engines and transformers (ISIC-3110) has steadily reduced their share of
imports in parts and components from 1996 to 2011. The import of parts and component for office, accounting
and computing industry has reduced from a share of 8.0 percent in 1996 to 2.31 percent in 2011. Exports in
final assembly are the exports of final commodities that are assembled and exported from Sri Lanka. It is the
difference between total exports in global production networks and the export of parts and components
following Athukorala (2016). A lion’s share of exports in final assembly commodities is concentrated in
clothing and clothing accessories (Standard International Trade Classification-SITC-84). The share of final
assembly exports from clothing and clothing accessories (SITC 84) was 87.5 percent in 1996. This share
gradually increased to a dominant position of 90 percent by 2011. Until 2005, the second and the third highest
export contribution was from travel goods (SITC 83) footwear and sport goods (SITC 85). In the post 2005
period, the significance of travel goods and footwear and sport goods declined drastically.
While economic theory offers several explanations for the pattern of trade between developed and
developing economies and its consequent effect on employment and wages, global production sharing models
mostly explain the link between global production sharing, employment and wages. Hence the main objective of
this paper is to examine the impact of global production sharing on employment and wages in the manufacturing
industry as postulated by the global production sharing models. As far as the author is aware, this is the only

The impact of global production sharing on employment and wages …
study that empirically examines the impact of global production sharing on manufacturing employment and
wages based on a panel dataset, the case of Sri Lanka.
II. Theory and literature
The main objective of this discussion is to lay down the theory of global production sharing, its
alternative models, and its predictions on employment and wages together with the available empirical
literature.
Literature on global production sharing and its impact on labour market outcomes fits the gambit of
new-trade theory (Joanna & Parteka, 2015). Global production sharing involves splitting the production process
into separate stages (Athukorala, 2006; Feenstra, 1998; Venables, 1999; Yeats, 2001) and the movement of
home-country jobs to other countries (Blinder & Kruger, 2013). Under global production sharing, each country
specialises in a specific stage of the production line in contrast to concentrated manufacturing. Therefore, much
of global production sharing encompasses trading intermediate products that entail further processing prior to
reaching the final consumer (Sanyal & Jones, 1982). It is a form of division of labour along the manufacturing
line based on skills differences (Timmer, Erumban, & Los, 2014). Therefore, comparative advantage is no
longer determined based on trading a final commodity (Kemeny & Rigby, 2012). Fragmentation is made
feasible as a result of reductions in trade barriers, transportation and information costs (Venables, 1999) leading
to a spatial separation of consumers and producers (Kemeny & Rigby, 2012). Heckscher-Ohlin and Ricardian
trade models are often consulted to explain production fragmentation (Gorg, 2000; Jones, Kierzkowski, &
Lurong, 2005). Under these models, the unskilled and labour intensive stages of production are shifted to
unskilled labour abundant developing economies, while the more technologically advanced stages of production
that requires skilled labour remain with developed economies (Hijzen, Gorg, & Hine, 2005; Kemeny & Rigby,
2012; Timmer et al., 2014; Zang & Markusen, 1999). Hence, the upstream knowledge-intensive jobs are
retained by the developed economies, while the downstream production functions of routine nature are allocated
to developing economies (Venables, 1999). Splitting up of production gives a country a comparative advantage
in commodities where it had no comparative advantage previously (Deardorff, 1998). As a result, southern
workers participating in fragmented production are predicted to receive higher wages relative to their
counterparts employed in domestic firms (Antras, Garicano, & Hansberg, 2006). However, a frequent criticism
is that leading firms that usually control value chains based in industrialised countries tend to take away the
profits from global production sharing (Derick, Kraemer, & Linden, 2010, 2011; Gereffi, Humphrey, &
Sturgeon, 2005; Gereffi & Korzeniewicz, 1994; Nathan, 2007). Having discussed the mechanism of global
production sharing, the reader’s attention is shifted to discuss the different models of global production sharing
that have evolved in the recent past. Although global production sharing comes in different models, the
predictions of these models are standardised. With the shift of unskilled and labour-intensive segments of
production to developing economies, it is predicted that the demand for labour and wages in these countries will
be positively affected.
Theoretical models that explain the growth of offshoring have proliferated (Kemeny & Rigby, 2012).
Ardnt (1997) considers a global production sharing model that consists of capital and labour as two factors of
production and two commodities, where one is labour-intensive and the other, capital-intensive. With
technological advancement when offshoring becomes possible, the labour-intensive sub stages of production are
allocated to countries that have a comparative advantage in labour-intensive production, and the capital-
intensive stages of production are allocated to capital-intensive countries. Grossman and Esteban (2008) propose
a model of task-trade where reduction in trade costs lead to increased offshoring on routine production
operations. Feenstra and Hanson (1995, 1997) proposed a global production-sharing model with a continuum of
goods and inputs. A range of activities leading to the final commodity including designing, creation, production,
packaging and delivery are identified and ranked in their order of labour intensity. Given two countries, where
one has a comparative advantage in skilled labour and the other in unskilled labour, the skilled stages of the
production are retained by the skills-intensive country while the stages of production that involve unskilled
labour are moved to the unskilled labour-intensive country. In another model, Feenstra and Hanson (1996, 2003)
considered a scenario where offshoring is an offshoot of international competition in industries producing
heterogeneous inputs subjected to differences in the relative demand for skilled and unskilled labour. Yeaple
(2005) introduced a model of heterogeneous firms and workers where international competition spurs more
productive firms to enter the export market thus raising the demand for skilled workers. Jones and Kierzkowski
(1988, 1990) in their model of global production sharing focus on production blocks and service links. Here,
when production is concentrated into one country, a fixed cost is involved. When production is sliced into
production blocks, it incurs only service link costs such as communication, transportation, coordination,
planning etc. Antras and Helpman (2004) developed a theoretical framework for studying global sourcing
strategies. This model predicts that in choosing between a domestic supplier and a foreign supplier for parts and

components, trade-off between lower variables costs in the South and the lower fixed costs in the North is of
critical importance.
Unlike the literature on factor proportions and technology theory, the available empirical evidence on
the impact of global production sharing is sparse. This limitation is more evident in the case of developing
economies. In terms of division of labour along value chains, Timmer et al. (2014) states that developed
economies are engaged in relocating the unskilled-labour-intensive production activities to developing
economies while the high value added activities are retained for their skilled workers. With the shift of
unskilled-labour-intensive sectors of the production process to developing economies, workers in these countries
are locked into monotonous unskilled assembly type of work. Unskilled work is poorly rewarded and affects the
supply of skills of a country on a longer-term basis. In an opposing argument, Broadman (2005) claims
economies that take part in global production sharing are increasingly experiencing a shift towards capital-
intensive or skilled labour-intensive parts production. Rajan (2003) states that developing economies which had
not been able to sell their final products in the export market earlier are now able to access international markets
under global production sharing. This is more beneficial for small economies that engage in the production of
parts and components of a vertically integrated manufacturing process (Jones et al., 2005). Irrespective of the
theoretical predictions, empirical evidence on global production sharing and its impact on employment and
wages in the case of developing economies is not so promising. A central theme that emerges from literature in
the context of developing economies is that global production sharing has been negative on both employment
and wages while its severity is more on unskilled workers. As an example, Kelegama (2009) identifies that the
flexibility provided by global production sharing to shift parts and components production from one country to
another has had detrimental effects on manufacturing employment in the case of Sri Lanka. Banga (2016)
analysed the impact of global production sharing on employment in India using a database of twenty-two
manufacturing industries over the period 1998-2011. Results using a fixed effects model indicated backward
linkages to have a replacement effect on employment and an insignificant effect on employment in the case of
forward linkages. Athukorala and Rajapathirana (2000a, 2000b); Kelegama and Foley (1999); Kelegama and
Wignaraja (1991); Knutsen (2003); Lopez and Robertson (2012, 2016) further supports this notion that
backward linkages under global production sharing in garments manufacturing are weak, ultimately leading to
lower value addition and employment (Athukorala & Santosa, 1997). In the case of Thailand, Athukorala and
Kohpaiboon (2013) found global production sharing to have a negative effect on manufacturing wages. Egger
and Egger (2002) analysed the impact of international outsourcing on the wages of workers in seven Central and
Eastern European countries over the period 1993-1998. They found a negative impact of international
outsourcing (export of intermediate goods) on the wages of workers. Egger and Egger (2003) analysed the
impact of foreign outsourcing on employment and wages using a panel of twenty Austrian two-digit industrial
dataset over the period 1990-1998. They found that outsourcing to Central and Eastern European and the former
Soviet Union significantly shifted relative employment in favour of high-skilled labour with a negative effect on
the unskilled workers.
In the case of developed economies, international outsourcing has been more promising with regards to
labour market outcomes for skilled workers compared to unskilled and less-educated workers. Ebenstein,
Harrison, McMillan, and Phillips (2014) analysed the impact of offshoring on workers in the United States using
population surveys. They concluded that offshoring had put downward pressure on US wages due to the
reallocation of workers away from higher-wage manufacturing jobs. This was mainly due to the outsourcing of
most unskilled and routine tasks to developing economies. Similarly, Mullen and Panning (2009) examined the
impact of international outsourcing on the employment of skilled workers in the United States using a panel of
six-digit manufacturing industries over the period 1997 to 2002. Their results indicated a negative effect of
outsourcing on the labour market outcomes for unskilled workers. Using four-digit level data for 450
manufacturing industries over the period 1958-1989, Paul and Seigel (2001) estimated the impact of foreign
outsourcing of employment in United States. They found outsourcing to have a relatively negative effect on the
demand for workers across all levels of education, with the strongest negative impact on workers with less than
a college degree.
McGregor, Stehrer, and deVries (2013) analysed the impact of international outsourcing on the demand
for labour using a sample of forty countries covering the period 1995-2009. Results indicated a negative effect
of offshoring on all skill levels, and the largest impact on medium skilled workers. For the United Kingdom,
Hijzen et al. (2005); Hijzen and Swaim (2007) examined the impact of international outsourcing on the skill
structure of labour demand using import-use matrices of input-output tables for manufacturing industries for the
period 1982-1996. Their findings indicated that international outsourcing had a strong negative impact on the
demand for unskilled workers and a positive impact on skilled workers. Later, in assessing the impact of
offshoring on employment in the European Union, Joanna and Parteka (2015) analysed the impact of
international outsourcing on employment in twenty-seven European countries across thirteen manufacturing

industries over the period 1995-2009. Findings indicated a negative impact of offshoring on domestic
employment while the intensity of the negative impact is more severe on unskilled workers.
On wage outcomes of global production sharing, a favourable effect is experienced by workers who are
more educated, more skilled and knowledge workers. For Denmark, Munch and Skaksen (2009) examined the
impact of outsourcing on wages of skilled and unskilled using a panel of manufacturing workers covering the
period 1993-2002. They found that international outsourcing had raised the wages of workers with further
education while lowering the wages of workers with basic and vocational education. Similarly, Hummels,
Jorgensen, Munch, and Xiang (2014) estimated the effect of offshoring on worker wages in Denmark using a
worker-firm-trade integrated database for the period 1995-2006. Findings indicated a positive effect on the
wages of skilled workers and a negative effect on the wages of unskilled workers.
III. Methodology
Data
Data for this research is captured from diverse sources. Manufacturing data is drawn from the Annual Survey of
Industries conducted by the Department of Census and Statistics. A distinctive feature of this rich manufacturing
dataset is the availability of data at the firm-level. This is a sample survey which supplements the industry
census conducted once in every 10 years, providing a nationally representative sample of manufacturing
industries. The survey takes the previous year as the reference period and includes all industrial establishments
with 5 persons or more. The Industry Census frame is used to determine the sample of establishments to be
surveyed each year. The Annual Survey of Industry sample includes 3500 to 4500 manufacturing firms each
year. The geographical strata contain all 25 administrative districts of Sri Lanka.
Trade data is drawn from the United Nations Commodity Tarde database. This database is maintained
by the United Nations Statistical Division. The database records import and exports data using several
commodity classification methods. For this study, the Standard Industry Trade Classification method at a 5-digit
level was used to extract import and export data for Sri Lanka with the rest of the world. Exports are valued on
free-on-board basis and imports are valued on cost-insurance-freight basis. Exports do not include re-exports.
The data on custom duties is captured using the Tariff Analysis Online database. The Tariff Analysis
Online database is maintained by the World Trade Organisation. This database maintains customs duties for
each commodity based on the Harmonised System of Coding at a 6-digit level. In addition to custom duties, the
government of Sri Lanka also charges a variety of tariffs on imports. Since these charges are outside the scope
of customs duties specified by the World Trade Organisation, these extra charges are commonly known as para-
tariffs or other levies. They include charges such as National Security Levy, Road infrastructure Development
Levy, Value Added Tax on imports, Excise duties, Ports Authority Levy etc. The tariff rates of these additional
levies are captured from the Tariff Guides prepared by Sri Lanka Customs. Tariff guides by Sri Lanka Customs
are maintained using HS Coding at a 6-digit and 8-digit level.
Conceptual framework
How has global production sharing impacted manufacturing employment and wages as predicted by the
global production sharing models? This is the research question this paper tries to address. The theoretical basis
for empirically testing this research question is provided by the global production sharing models. Classical and
the neo-classical trade models based on the exchange of finished commodities fail to capture the fragmentation
of commodity production under global production sharing (Kemeny & Rigby, 2012). Moreover, the introduction
of trade in parts and components necessitates a fundamental alteration in the classical analysis to reflect the
dynamic conditions under which global production sharing takes place (McKenzie, 1954). Under global
production sharing, trade in numerous parts and components takes place simultaneously with multiple trading
partners (Yeats, 2001). With the splitting-up of the production process into parts and components, developing
countries now have an opportunity to engage in the parts and components trade that is predicted to positively
influence the demand for labour and wages (Ardnt, 1997; Feenstra, 2004; Feenstra & Hanson, 1995, 1996,
2003). Hence the research question stated above can be formulated into the following hypothesis for empirical
examination.
Hypothesis: Participation in global production sharing has a positive impact on manufacturing employment and
wages in developing economies.
This study is an attempt to model manufacturing labour and wages in response to trade stimulus.
Therefore, in the labour demand model, the dependent variable is manufacturing labour (L). Similarly, in the
wages model, the dependent variable is average real wages (W). Based on the hypothesis developed, ‘global
production sharing’ is the key independent variable in this instance. ‘Global production sharing’ is depicted by
four proxy variables for robustness of the results [the share of imports in parts and components (P&C_IM), the

share of exports in parts and components (P&C_EX), the share of exports in global production sharing
(GPN_EX) and the share of parts and components in total trade (P&C_TR)].
What follows is a brief discussion about the method of measuring global production sharing, followed
by the operationalisation of proxy variables. Delineating parts and components from trade data are a critical
aspect of measuring global production sharing. Therefore, the method of delineating import and export of parts
and components from trade data is initially explained. Secondly, the variables for capturing the impact of global
production sharing are explained. Measuring global production sharing is not a straightforward exercise (Blinder
& Kruger, 2013; Kemeny & Rigby, 2012). Different methodologies are used to capture the effect of offshoring.
Some empirical literature on international outsourcing use two measures to capture its effect following Feenstra
and Hanson (1996, 1999). The broad measure is defined as the value of all imported intermediates of an
industry. The narrow measure restricts attention to intermediate inputs that are purchased from the same
industry as the good being produced. Due to the complexity involved in measuring trade in global production
sharing, literature has often used trade in intermediate inputs or trade in parts and components to proxy for
global production sharing (Audet, 1996; Campa & Goldberg, 1997; Hummels, Ishii, & Yi, 2001; Yeats, 2001).
The main drawback of focusing on intermediates is that it necessarily excludes offshoring assembly activities
(Hijzen & Swaim, 2007). Therefore, it is important to capture the effect of the export of parts and components
and the import of parts and components in production sharing (Clark, 2010). Therefore, without explicitly
modelling for parts and components in trade, an analysis of international trade in global production sharing will
be misleading (Athukorala & Menon, 2010; Athukorala & Talgaswatta, 2016) given the importance stressed on
service links in global production sharing (Jones & Kierzkowski, 1990). Similarly, trade in parts and
components has grown at a faster rate than trade in final goods and trade in parts and components behave
differently to trade in final goods (Athukorala, 2013; Athukorala & Yamashita, 2006; Yeats, 2001). A number
of studies have also used the input-output technique to measure the degree of manufacturing production and
trade of selected countries in global production sharing (Hummels et al., 2001; Hummels, Rapoport, & Yi, 1998;
Johnson & Noguera, 2012). On the other hand, empirical literature has also used trade flows between US
multinational enterprises and their foreign affiliates (Hanson, Mataloni, & Slaughter, 2005) while engaging the
Dictionary of Occupational Titles for identifying the routine and non-routine tasks structure of activities in
measuring the extent of global production sharing (Kemeny & Rigby, 2012). Given the availability of data and
empirical limitations, global production sharing in the context of this thesis is modelled after trade in parts and
components. Regardless of its downward bias, trade in parts and components is the only measure of fragmented
trade (Athukorala & Yamashita, 2006).
Trade in global production sharing involves trade in parts and components and trade in final assembly
goods. It is important to note the distinction between trade in ‘part and components’ and ‘intermediate
commodities’ at the outset since these two terms are used interchangeably in literature vis-à-vis global
production sharing. Parts and components are a subset of intermediate goods and are inputs further along the
production chain. Unlike intermediate inputs, parts and components do not have a reference price, and are not
sold on exchanges, demanded under a contractual environment and most importantly do not have a commercial
life of their own (Nunn, 2007). Trade in intermediate inputs has often been used in literature to capture the
effects of offshoring (Foster, Stehrer, & deVries, 2013; Muller & Panning, 2009). However, the main drawback
of focusing on intermediate inputs is that it excludes the offshoring of assembly activities (Hijzen & Swaim,
2007). Therefore, an essential element in analysing trade in global production networks is to systematically
delineate items of trade in parts and components and trade in final assembly from trade records (Yeats, 2001).
One of the main reasons for the dearth of research on the impact of global production sharing is due to the lack
of clarity as to which trade can be categorised as trade under global production networks and how to measure it
(Banga, 2014). SITC revision 1 made it impossible to delineate parts and components in international trade.
SITC revision 2 adopted a more detailed classification of parts and components, although to a limited extent
(Athukorala & Yamashita, 2006). With the introduction of SITC revision 3, these difficulties have been
countered, allowing one to approximate trade in global production sharing (Yeats, 2001).
Given the importance and the emphasis on the need to separate parts and components from trade flows,
the analysis on trade in parts and components is based on a list meticulously prepared by Athukorala and
Talgaswatta (2016) using SITC revision-3. This list identifies three hundred and fifty-five items of parts and
components traded under global production sharing. Having identified the commodities that are traded as parts
and components under global production sharing, Athukorala (2016) follows a simple method to identify trade
in final assembly. As Krugman (2008) points out, there is no hard and fast rule to make a distinction between
trade in parts and components and trade in final assembly products. The only feasible method available to
delineate parts and components from final assembly products is to concentrate on the specific product categories
in which network trade is concentrated. Based on the literature, Athukorala (2016) identifies fourteen product
categories in which global production sharing is highly prevalent. Once these product categories are identified, a
reasonable assumption is made that these product categories contain virtually no product that is produced from

the start to its end in a given country. Therefore, the difference between the value of total exports and the export
value of parts and components within these product categories is treated as the export value of final assembly
goods.
Athukorala (2016) identifies the following product categories where trade in global production sharing
mostly takes place. They include, power generating machinery (SITC-71), specialised industrial machines
(SITC-72), metal working machines (SITC-73), general industrial machinery (SITC-74), office machines and
automatic data processing machines (SITC-75), telecommunication and sound recording equipment (SITC-76),
electrical machinery (SITC-77), road vehicles (SITC-78), other transport equipment (SITC-79), travel goods
(SITC-83), clothing and clothing accessories (SITC-84), footwear and sport goods (SITC-85), professional and
scientific equipment (SITC-87), and photographic apparatus (SITC-88). Of these SITC categories, 83, 84 and
85 are identified as buyer–driven production networks, while the remaining are producer-driven production
networks. Once items traded as parts and components have been identified, these items have to be traced to their
respective industry categories using a concordance prepared by the European Commission. This concordance
matches all trade items in SITC coding to their respective industries based on ISIC revision 3.1. Delineation of
parts and components enables us to construct the required regressors to capture the effects of Global Production
Sharing. However, when moving forward in the modelling exercise, the regressor that is theoretically sound and
with statistical power will be selected based on the principal of parsimony(Harvey, 1990).
Modelling
Following Hine and Wright (1998) and Greenaway, Hine, and Wright (1999) Milner and Wright (1998), this
paper uses the Cobb-Douglas production function of the following form, which serves as the core model in this
analysis;
……………………..(1)
where for the representative firm in industry i in period t; Q = real output; K = capital stock; L= units of labour
utilised; A = technology and α, β represent the factor share coefficients while ˠ allows for factors changing the
efficiency of the production process. Based on this, the following standard labour demand model equation (2) is
derived, where Lit is total employment, Wit is average real wages, Qit is the real output and Kit is real capital
intensity in industry ‘i’ at time ‘t’ and Xit is a vector of variables which affect the efficiency of production, so it
is related to Aˠ. The vector of variables includes the key independent variable and other control variables that
impacts manufacturing employment. The θ0 is the overall intercept and θ1, θ2, θ3 and θ4 are unknown slope
parameters to be estimated and the error term uit. A profit maximising firm employs labour where the marginal
revenue product of labour equals the wage (W).
……………(2)
Similarly, wages are determined by numerous factors, and following Greenaway et al. (1999); Hine and
Wright (1998); Milner and Wright (1998), and the following wages equation is derived (3), where Wit is average
real wages, Lit is total employment, Qit is the real output and Kit is real capital intensity in industry ‘i’ at time
‘t’. Xit represents a vector of variables that are engaged in the wage setting process and includes the key
independent variable and other control variables that impacts wages. The β0 is the overall intercept and β1, β2,
β3 and β4 are unknown slope parameters to be estimated and the error term ɛit.
…………..(3)
Variables
In the labour demand model, manufacturing labour is the main outcome variable. In the wages model, the main
outcome variable is manufacturing wages. The research question is about analysing the impact of global
production sharing on manufacturing employment and wages. Based on the hypothesis developed, ‘global
production sharing’ is the key independent variable in this instance. ‘Global production sharing’ is depicted by
four proxy variables for robustness of the results [the share of imports in parts and components (P&C_IM), the
share of exports in parts and components (P&C_EX), the share of exports in global production sharing
(GPN_EX) and the share of parts and components in total trade (P&C_TR)]. The study alternates between
several proxy measures of global-production sharing in ensuring the model’s sensitivity and the robustness of
the empirical findings. The share of imports in parts and components (P&C_IM) is estimated as the value of real
imports in parts and components expressed as a percentage of total real imports following Kohpaiboon and
Jongwanich (2013, 2014). It indicates the extent to which an industry is engaged in global production sharing.
The higher the import share, the more the industry is involved in global production sharing. The share of exports
in parts and components (P&C_EX) is estimated as the value of real exports in parts and components expressed
as a percentage of total real exports. The share of exports in global production sharing (GPN_EX) is estimated as

the value of real exports in global production networks expressed as a percentage of total real exports. Exports
in global production sharing include exports in parts and components and exports in final assembly. The share
of parts and components in total trade (P&C_TR) is estimated as the value of total real exports and imports in
parts and components expressed as a percentage of total real manufacturing imports and exports following
Kohpaiboon and Jongwanich (2013, 2014). Firms might be engaged in global production sharing as parts and
components suppliers and therefore the focus on the import of parts and components alone might be misleading.
Therefore, trade instead of imports is used to mitigate such a problem.
We also use few control variables in the analysis; manufacturing output (Q), capital intensity (KI), skills
intensity (SKI), marginal efficiencies of production (MEP), import penetration ratio (IP), custom duties (CD)
and para tariffs (PT).
The operational definitions of the variable are presented accordingly in table 1.
Table 1: Variable definition
Variable Definition
L Labour is expressed in its natural logarithmic form. This includes all type of
manufacturing labour.
Q Real output is expressed in its natural logarithmic form.
W Average real wages are expressed in its natural logarithmic form.
K Capital intensity is the real output divided by real value of machinery,
expressed in its natural logarithmic form.
EI Export-intensity is the value of real exports expressed as a percentage of real
output
IP Import penetration is measured as the value of real imports expressed as a
percentage of real consumption
SKI Skills-intensity is estimated by expressing the share of skilled factory
operatives as a percentage of total manufacturing workers
MEP Marginal efficiency of production is measured as the average plant size of the
top fifty percent of firms expressed as a percentage of total output
CD Industry structure variables Custom duties is estimated using the simple
average tariff of all tariff lines for each four-digit manufacturing industry.
PT Para-tariffs is estimated using the simple average of all para-tariff lines at each
four-digit manufacturing industry level.
RDI Research and development intensity is measured as the share of technicians in
total manufacturing employment
P&C_IM The share of imports in parts and components is estimated as the value of real
imports in parts and components expressed as a percentage of total real imports
P&C_EX The share of exports in parts and components is estimated as the value of real
exports in parts and components expressed as a percentage of total real exports.
GPN_EX The share of exports in global production sharing is estimated as the value of
real exports in global production networks expressed as a percentage of total
real exports. Exports in global production sharing include exports in parts and
components and exports in final assembly.
P&C_TR The share of parts and components in total trade is estimated as the value of
total real exports and imports in parts and components expressed as a
percentage of total real manufacturing imports and exports
Analytical technique
Given that the research question is theory testing and the data is quantitative in nature, a quantitative method of
analysis is adopted. Therefore, a multiple regression analysis technique is engaged to empirically analyse the
impact of global production sharing on employment and wages in the manufacturing industry. This study is
based on a trade and industry panel data set covering the period 1994 to 2011. Given that the dataset is a panel, a
panel data technique should be employed in the analysis of data. Initially, a pooled ordinary least squares
regression was conducted. Accordingly, a Breusch-Pagan Lagrangian multiplier test was conducted to determine
whether the pooled regression was consistent or not. The estimated test results were significant, implying that
pooled regression is not an appropriate method in this case. Given this, researchers often use a fixed effects

model or a random effects model in analysing panel data. Following the Hausman (1978), the fixed effect
estimation model is engaged.
Regression diagnostics
A preference for economic theory over method was followed as recommended strongly by Studenmund (2001)
for selecting variables to the model. To complement, several alternative formal model specification tests were
performed. This study is based on a trade and industry panel data set covering the period 1994 to 2011 and is an
unbalanced panel dataset with gaps. To avoid inflation leading to spurious correlation, all nominal values have
been adjusted for inflation to be comparable across different time points. Spurious correlation is caused by
nonstationary time series. Accordingly, both the Augmented Dickey Fuller test and the Phillips-Perron unit-root
tests were conducted. The null hypothesis was rejected at less than 1 percent level of statistical significance.
Several tests for multicollinearity such as the correlation matrix and the sensitivity of the model when adding
and deleting independent variables were checked. When tested for multicollinearity, the variance of inflation
factor was less than 1.5 except in the case of two variables yet, well below the standard. Models were also tested
for heteroscedasticity. Since the dataset we have is a panel that is unbalanced and with gaps, there is no robust
mechanism that tests for heteroscedasticity, when using a fixed effects model. Given the limited options,
heteroscedasticity is tested using a graphical method of detection (Gujarati, 1998). The models were also tested
for serial correlation. The DW statistics for both the labour demand and the wages model converges to 2,
thereby indicating no serial correlation. Having conducted different tests and precautions in modelling for labour
demand and wages, their robustness is also investigated. The estimated labour demand model is robust in
conventional statistical terms. Drawing attention to the final labour demand model, the model is statistically
significant at less than one percent as indicated by the F-statistics. It possesses a high level of explanatory power
as indicated by the adjusted R-squared (0.8431). On the wages model, according to the conventional statistical
measures, the estimated model is robust. The wages model is statistically significant at less than one percent as
indicated by the F-statistics. It possesses a satisfactory level of explanatory power as indicated by the adjusted
R-squared (0.4846).
IV. Results and discussion
The results of the labour demand model are presented in table 2 below.
Labour demand model
Table 2: Labour demand model-Impact of global production sharing on
manufacturing employment
1 2 3 4
Q 0.7106a 0.7101a 0.7104a 0.7115a
[61.66] [61.33] [61.63] [61.62]
W -0.5307a -0.5313a -0.530a -0.5309a
[-16.20] [-16.19] [-16.19] [-16.21]
K -0.0758a -0.0754a -0.0758a -0.0766a
[-6.95] [-6.91] [-6.95] [-7.02]
EI 0.00001a 0.00001a 0.00001a 0.00001a
[2.56] [2.55] [2.55] [2.63]
IP 0.00004b 0.00004b 0.00004b 0.00004c
[1.99] [2.02] [1.99] [1.87]
SKI 0.0033a 0.0033a 0.0033a 0.0033a
[3.65] [3.64] [3.66] [3.63]
MEP -0.0038a -0.0039a -0.0038a -0.0038a
[-7.06] [-7.12] [-7.06] [-7.04]
PT -0.0052a -0.0052a -0.0053a -0.0052a
[-5.23] [-5.30] [-5.33] [-5.24]
RDI -0.0060c -0.0061c -0.0061c -0.0060c
[-1.88] [-1.91] [-1.92] [-1.89]

P&C_IM -0.0027
[-1.45]
P&C_EX -0.0004
[-0.37]
GPN_EX -0.0018
[-1.20]
P&C_TR -0.0034c
[-1.80]
Constant -1.0201a -1.0344a -1.0006a -1.0256a
[-3.14] [-3.18] [-3.07] [-3.16]
N 1327 1327 1327 1327
R2 0.8437 0.8479 0.8422 0.8431
F 16.04a 15.95a 15.91a 16.07a
t-values in parentheses. a Significance at 1 percent, b at 5 percent, c at 10 percent
The coefficient on the share of imports in parts and components is negative on manufacturing
employment, although statistically insignificant. Similarly, the coefficient on the share of exports in parts and
components is also negative and statistically insignificant on manufacturing employment. In the same manner,
the coefficient on the share of exports in global production exports is also negative and statistically insignificant
on manufacturing employment. However, the coefficient of the share of parts and components in total trade is
negative and statistically significant on manufacturing employment. Therefore, all the regressors that capture the
effects of global production sharing are negative on manufacturing employment and are in the same direction.
Therefore, based on theoretical soundness and the statistical power of the regressor on the share of parts and
components in total trade, this variable is selected to represent global production sharing in the final model.
Given that the coefficient on the share of parts and components in total trade is negative and statistically
significant on manufacturing employment, the empirical findings are against theoretical predictions.
Theoretically, developing economies are predicted to gain on employment and wages by engaging in global
production sharing. However, empirical findings adamantly contradict the theoretical predictions. Although the
findings are against the theoretical expectations, similar results from developed and developing economies are
reported following Banga (2016); Joanna and Parteka (2015); Mullen and Panning (2009); Paul and Seigel
(2001). However, there are empirical findings that have reported a positive effect of global production sharing
on manufacturing employment following Egger and Egger (2003); Hijzen and Swaim (2007).
The empirical findings discussed above on the impact of global production sharing on manufacturing
employment are against theoretical expectations. As opposed to the conventional trading along final
commodities, trade along the lines of global production sharing forges fewer domestic linkages to achieve
growth in employment. Hence, low value addition criterion under global production sharing create less scope for
employment creation. On the other hand, the share of exports in global production sharing is on a declining
trend as a reaction to the less conducive macroeconomic conditions that in turn affect manufacturing
employment negatively. Similarly, global production sharing in Sri Lanka is highly concentrated into a single
labour-intensive and low technology product in which the demand is on a declining trend with detrimental
effects due to competition posed by new market entrants. Moreover, the labour-intensive segments of
production that are transferred to developing economies are often capital-intensive in nature for these
developing economies. In turn, these capital-intensive foreign technologies have a negative impact on
manufacturing employment. What follows is a more elaborative discussion on few of the possible reasons for
the negative effect of global production sharing on manufacturing employment in Sri Lanka.
Poor backward linkages and low value addition
The effect of backward linkages in a global production environment could be multi-fold. When parts
and component imports are complementary to existing domestic resources, a positive effect on employment
could be expected. A negative effect is expected when parts and component imports are substitutes of domestic
production (Davis & Prachi, 2007). Import liberalisation in Sri Lanka displaced labour from import-substitution
industries, while numerous industries that had the potential to be vertically linked were wiped out (Kelegama &
Wignaraja, 1991) as manufacturing exports from Sri Lanka are highly import-intensive it leads to fewer
backward linkages to other sectors of the economy (Athukorala & Rajapathirana, 2000a, 2000b). As an
example, textile and wearing apparel industry that is highly linked to global production sharing imports an
estimated 80-90 percent of its inputs- even items such as fabric, buttons, zippers, studs, eyelets, buckles,

interlining, padding and corrugated cartons which can be procured by the local suppliers (Lopez & Robertson,
2012). This makes the garment industry the lowest in terms of value addition (Kelegama & Foley, 1999). Low
value addition, leads to low employment opportunities. On the other hand, according to Athukorala and Santosa
(1997), forging domestic linkages to achieve growth in employment is not possible in an era of global
production sharing. They argue that the conventional value addition criteria cannot be applied when sourcing
inputs under global production sharing as it is determined as a part of an overall process of international
procurement (Athukorala & Santosa, 1997). For example, garment manufacturers have to follow instructions on
where to purchase their fabrics, inputs and machinery to ensure the right quality and uniformity of the product.
Therefore, local producers of intermediate supplies are easily by-passed under global production sharing leading
to their ultimate displacement (Knutsen, 2003) as export-oriented firms are poorly linked to domestic market
oriented firms (Kelegama & Foley, 1999). Moreover, under global production networks, it is the lead firms in
industrialised economies that perform most of the value added activities (Cardero, Mantey, & Mendoza, 2006)
while the less-value added activities are outsourced to developing economies (Lopez & Robertson, 2016).
Therefore, in a global production environment, the relevance of the conventional domestic value addition
criteria in generating economic growth and employment opportunities in a developing country context such as
Sri Lanka is doubtful.
Declining share of exports in global production sharing
Trade under global production sharing is sensitive compared to trade in final commodities in a
traditional manufacturing environment. Due to its segmented nature, manufacturers practicing global production
sharing are in a flexible position to shift production from one country to another. For example, Sri Lanka was an
attraction to quota hopping manufacturers under the Multi-Fibre Agreement. These opportunistic investors
came to Sri Lanka to take advantage of the unmet quota allocations when they had exhausted the quota’s in East
Asia (Kelegama, 2009). Once these investors recouped their investments, coupled with rising wages, removal of
tax incentives, abolition of the Multi-Fibre Agreement, and the suspension of the Generalised System of
Preferences, these manufactures shifted production to other low-cost destinations. In Sri Lanka, the share of
exports in global production sharing as a percentage of total exports has continued to decline from 33.0 percent
from 2001 onwards. Similarly, the share of exports in global production sharing as percentage of manufacturing
exports has continued to shrink from 56.7 percent from 2000 onwards. In terms of GDP, exports in global
production networks in GDP has continued to erode from a share of 10.8 percent in 2000 to 5.0 percent by 2014.
In the case of exports in parts and components, the share of exports in parts and components in manufacturing
exports has reduced from 10.25 percent from 2001 onwards [Table 3]. Since the demand for labour is a derived
demand, the declining share of exports in global production exports has negatively affected manufacturing
employment.
Table 3: Manufacturing exports in global production sharing
Exports
in
global
production
networks
as
a
percentage
of
manufacturing
exports
Exports
in
global
production
networks
as
a
percentage
of
total
exports
Buyer
driven
exports
in
global
production
sharing
as
a
percentage
of
manufacturing
exports
Share
of
buyer
driven
exports
in
global
production
sharing
in
total
exports
in
global
production
exports
Share
of
producer-driven
exports
in
global
production
sharing
in
total
exports
in
global
production
sharing
Share
of
exports
in
global
production
sharing
as
a
percentage
of
GDP
Share
of
parts
and
component
exports
as
a
percentage
of
manufacturing
exports
1994 47.15 26.8 42.71 90.58 9.42 7.3 4.73
1995 56.08 33.9 50.83 90.64 9.36 9.9 5.82
1996 56.10 33.8 49.62 88.44 11.56 10.0 6.97
1997 58.63 36.1 50.82 86.67 13.33 11.1 8.09
1998 57.50 35.9 50.22 87.35 12.65 10.9 7.75
1999 57.19 31.7 50.39 88.11 11.89 9.3 7.05

2000 56.73 32.3 48.30 85.14 14.86 10.8 9.00
2001 57.10 33.0 47.43 83.06 16.94 10.1 10.25
2002 51.57 29.1 44.08 85.48 14.52 8.2 7.83
2003 50.03 26.2 42.80 85.54 14.46 7.4 8.21
2004 50.83 27.6 40.74 80.14 19.86 7.9 11.33
2005 46.06 26.9 38.76 84.16 15.84 7.0 8.04
2006 47.12 27.9 38.33 81.35 18.65 6.8 9.46
2007 46.88 26.9 37.02 78.96 21.04 6.4 10.32
2008 46.31 28.4 38.53 83.19 16.81 5.7 7.91
2009 49.07 28.4 43.33 88.30 11.70 4.8 5.44
2010 47.22 26.5 39.35 83.33 16.67 4.6 6.50
2011 47.12 28.6 39.28 83.37 16.63 5.1 6.37
2012 49.17 29.6 40.97 83.33 16.67 4.9 6.77
2014 59.19 29.1 40.12 83.35 16.65 5.0 6.57
Source: Computed by the author based on UN Comtrade database
Global production sharing concentrated into few industries
Global production sharing is not substantial enough to warrant a significant impact on employment and
wages in developing economies (Hijzen et al., 2005). More than 80 percent of exports in global production
exports in Sri Lanka are concentrated into buyer-driven exports [Table 4]. When even buyer-driven exports in
global production sharing are analysed, an excessive concentration on one specific manufacturing industry is
observable. This is a major cause for the negligible effect of global production sharing on manufacturing
employment and wages. Of the buyer-driven exports in global production, the share of exports in global
production networks in wearing apparel accounted for 78.8 percent followed by exports in luggage, handbags,
items like saddlery (7.5 per cent) and exports in other rubber products (6.4 per cent) in 1996. By 2011, exports
in wearing apparel had consolidated into a lion’s share of buyer-driven exports in global production sharing
(90.8 per cent). Therefore, although wearing apparel industry is linked to global production networks, other
manufacturing industries are poorly linked to global production networks thus exerting a minimal impact on
manufacturing employment.
Table 4: Buyer-driven exports in global production sharing by year and manufacturing industry
ISIC Description 1996 1999 2002 2005 2008 2011
1730 Manufacturing of knitted, crocheted fabrics 2.9 2.2 2.1 1.8 0.9 0.9
1810 Manufacturing of wearing apparel 78.8 77.7 84.9 88.7 90.8 90.8
1820 Dressing, dying of fur 0.0 0.0 0.0 0.0 0.0 0.0
1912 Manufacture of luggage, handbags 7.5 10.0 4.8 1.4 0.3 0.1
1920 Manufacturing of footwear 3.8 5.3 1.5 0.9 1.2 0.6
2519 Manufacturing of other rubber products 6.4 4.4 5.7 6.6 6.4 7.4
2520 Manufacturing of plastic products 0.2 0.5 0.9 0.7 0.4 0.2
100.0 100.0 100.0 100.0 100.0 100.0
Source: Computed by author based on UN Comtrade database
Capital-intensive [labour-saving] nature of activities outsourced
The impact of global production sharing on employment depends on the relative factor intensity of the
stage of production allocated (Helleiner, 1973; Venables, 1999). Developed countries transfer the labour-
intensive segments of production to developing countries abundant in labour. These labour-intensive segments
are relatively capital-intensive and labour-saving in nature for developing countries (Cardero et al., 2006;
Feenstra, 1998; Feenstra & Hanson, 1995, 2003; Lopez & Robertson, 2016). These capital-intensive production
segments require skilled labour. Therefore, a shift from unskilled to skilled labour could be observed in
developing economies (Cardero et al., 2006; Devadason, 2007). This phenomenon is also evidenced in Sri
Lanka. With global production sharing, a radical shift from unskilled labour to skilled labour is evidenced. By
2014, more than 75 percent of the factory operatives were skilled workers and a minority were unskilled
operatives, confirming that the work outsourced to Sri Lanka is skilled and capital-intensive in nature.

Wages model
‘How has global production sharing impacted manufacturing wages as predicted by the global
production sharing models?’. This is the next research question of this paper. Drawing on this, the conceptual
framework hypothesized that ‘participation of global production sharing has a positive impact on manufacturing
employment and wages in developing economies’. In this section, the focus is on ascertaining the impact of
global production sharing on ‘manufacturing wages’. Following the hypothesis drawn, the key independent
variable is ‘global production sharing’ depicted by four proxy variables [the share of imports in parts and
components (P&C_IM), the share of exports in parts and components (P&C_EX), the share of exports in global
production sharing (GPN_EX) and the share of parts and components in total trade (P&C_TR)]. The dependent
variable in the analysis is average real wages (W), while the analysis controls for other factors that influence
manufacturing wages.
Table 5: Wages Model- Impact of global production sharing on manufacturing
wages
1 2 3 4
Q 0.3547a 0.3550a 0.3544a 0.3553a
[22.74] [22.80] [22.74] [22.74]
L -0.3307a -0.3300 a -0.3304a -0.3311a
[-15.99] [-15.98] [-15.98] [-16.00]
K -0.0172b -0.0169b -0.0172b -0.0176b
[-1.94] [-1.92] [-1.94] [-1.98]
EI 0.00001c 0.00001c 0.00001c 0.00001c
[1.67] [1.73] [1.66] [1.70]
IP 0.00002 0.00002 0.00002 0.00002
[1.44] [1.35] [1.44] [1.38]
SKI 0.0023a 0.0023a 0.0023a 0.0023a
[3.19] [3.19] [3.19] [3.18]
MEP -0.0005 -0.0005 -0.0005 -0.0005
[-1.16] [-1.19] [-1.15] [-1.15]
CD 0.0028a 0.0028b 0.0029b 0.0028b
[2.28] [2.22] [2.27] [2.26]
RDI 0.0108a 0.0108a 0.0108a 0.0108a
[4.28] [4.29] [4.27] [4.28]
P&C_IM -0.0009
[-0.62]
P&C_EX -0.0011
[-1.27]
GPN_EX -0.0006
[-0.48]
P&C_TR -0.0013
[-0.85]
Constant 5.6296a 5.6215a 5.6357a 5.6261a
[27.75] [27.73] [27.67] [27.75]
N 1327 1327 1327 1327
R2 0.4873 0.4878 0.4806 0.4846
F 7.18a 7.24a 6.78a 7.20a
t-values in parentheses. a Significance at 1 percent, b at 5 percent, c at 10 percent
The coefficient on the share of imports in parts and components is negative and statistically
insignificant on manufacturing wages. The coefficient on the share of exports in parts and components is

negative and statistically insignificant on manufacturing wages. Similarly, the coefficient on the share of exports
in global production sharing on wages is negative and statistically insignificant. Finally, the coefficient on the
share of parts and components in total trade is also negative and statistically not significant on manufacturing
wages. Given that all four regressors are statistically insignificant, there is no conclusive evidence on the impact
of global production sharing on manufacturing wages. Empirical evidence from other countries on the impact of
global production sharing on wages has been in both directions; either positive or negative. Global production
sharing is found negative on wages following Egger and Egger (2002); Hummels et al. (2014); Kohpaiboon and
Jongwanich (2013); Mullen and Panning (2009), while it is reported to have a positive effect on wages
following Autor, Katz, and Kruger (1998); Hanson and Harrison (1999); Kohpaiboon and Jongwanich (2014).
All regressors on global production sharing were adamantly negative on wages, although statistically
insignificant. Therefore, the impact of global production sharing on manufacturing wages in Sri Lanka is
inconclusive. The discussion below is centred around to the question of why manufacturing wages in most
instances does not react to global production sharing in a developing country context as theoretically predicted.
Producers controlling production chains do not always share the principal profits from global production sharing
among workers in developing economies (Gereffi & Korzeniewicz, 1994). Power relations along production
chains determine what activity is to be allocated to which production node (Gereffi et al., 2005). In other words,
the lead firm decides on the chain surpluses, its appropriation, how much of the surplus will be reinvested in
which node and what nodes will cover adjustment costs when surpluses are low (Knutsen, 2003). Usually, lead
firms in developed countries extract surpluses from the periphery by coordinating and controlling the links
(Gereffi & Korzeniewicz, 1994). On the other hand, the peripheral countries are only engaged as platforms to
export low technology, labour-intensive goods made using low-waged labour (Lopez & Robertson, 2016).
Retaining wage growth in the periphery is possible as multinational corporations have the ability to shift
production from one destination to another due to the nature of global production sharing. These threats of
production shifting are powerful enough to slow the real wage growth in developing economies (Athukorala &
Kohpaiboon, 2013). As a result, the generated profits are taken away by transnational companies based in
developed countries that are in control of the entire value chain of designing, marketing, and retailing activities
(Derick et al., 2011). This impedes reinvestments in technological upgrading making suppliers more vulnerable
to competition in the longer term which would have negative effect on employment and wages (Knutsen, 2003).
Case studies from China show that for the Apple iPod, only 4 USDs are attributed to the producers located in
China out of the 150 USD, while the rest of the value accrues to Japan, South Korea, and the US (Derick et al.,
2010). Since capital is mobile across countries and labour is easily substitutable (in the case of unskilled labour-
intensive manufacturing), the dominant firms that control these supply chains intimidate small producers with
the threat of exit or the relocation of capital elsewhere (Nathan, 2007). Such threats result in squeezing both
wages paid to workers and prices paid to small producers (Kaplinsky, 2005).
V. Conclusion
Recasting on whether the final research objective was achieved, the aim was to empirically examine
the impact of global production sharing on employment and wages in the manufacturing industry. The
theoretical basis for this empirical examination was drawn from the Global production sharing models.
Considering the impact on manufacturing employment using labour demand model, the impact of global
production sharing is negative and statistically significant on manufacturing employment against theoretical
expectations. On the other hand, on the impact on wages using the wages model, all proxy explanatory variables
on global production sharing display signs against the theoretical expectation, while the results are statistically
not significant. Therefore, the results on the impact of global production sharing on manufacturing wages is
inconclusive.
Acknowledgement
I am grateful to Ms Anjalin Balalingham for doing an excellent job in proofreading this paper.
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